---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- yanolja/EEVE-Korean-10.8B-v1.0
- upstage/SOLAR-10.7B-v1.0
base_model:
- yanolja/EEVE-Korean-10.8B-v1.0
- upstage/SOLAR-10.7B-v1.0
---

# TEST_MODEL

TEST_MODEL is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0)
* [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)

## 🧩 Configuration

```yaml
base_model: yanolja/EEVE-Korean-10.8B-v1.0
dtype: float16
experts:
  - source_model: yanolja/EEVE-Korean-10.8B-v1.0
    positive_prompts: ["You are an helpful general-pupose assistant."]
  - source_model: upstage/SOLAR-10.7B-v1.0
    positive_prompts: ["You are helpful assistant."]
merge_method: slerp
gate_mode: cheap_embed
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5

tokenizer_source: base
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jieunhan/TEST_MODEL"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```